Throughout the morning we've heard
about the incredible promise of exploring intelligence.
The potential to deeply understand how humans learn.
To use that understanding together
with novel computational methods to create new algorithms,
and to apply those methods to virtually
every scientific discipline, into every aspect
of everyday life.
Finance, transportation, health care, social interactions.
One might be inclined to infer from this
that the impact of intelligent algorithms
are mostly still to come.
And that impact is high.
But in many cases, the impact is already here.
Tang Xiao'ou and his company sense time
are a wonderful example of that.
An MIT PhD graduate from 1996, Xiao'ou
has decades of experience in computer vision and machine
learning.
I had the pleasure of serving on his thesis committee.
His supervisor was one of my first students,
and I'd been delighted to stay in contact with Xiao'ou
ever since.
Currently professor at the Chinese university of Hong Kong
and a former lead researcher at Microsoft Asia,
Xiao'ou is one of the most visible and influential leaders
in artificial intelligence, both within China
and internationally.
Since time, Hong Kong's first unicorn
produced his world leading systems
for face recognition and verification,
and other video analytics.
By building on decades of careful academic research,
and serves as a great example of how robust
scientific development of methods
can have impact in real world settings.
Xiao'ou is not only conducting world class academic research
and building a cutting edge company,
he's also wonderfully maintaining
his longstanding and close ties to MIT.
And thus I'm pleased to note yesterday's announcement
of the MIT sense time alliance on artificial intelligence.
A partnership that will open up new avenues of discovery
across MIT, in areas like computer vision,
human intelligence inspired algorithms, medical imaging
and robotics.
Will drive technological breakthroughs in AI
that have the potential to confront
some of the great challenges of the world.
And empower MIT and faculty, both faculty and students,
to pursue interdisciplinary projects at the vanguard
of intelligence research.
I'm thus delighted to introduce to you Tang Xiao'ou.
Thank you.
Thank you Eric and thank you MIT for giving
me the this opportunity to speak on this stage.
It's truly nice to be back home.
It's a lot colder than in Hong Kong, but I do feel warm.
I mean, I talk to Eric, I talk to Anessa, I talk to Josh.
I have this warm feeling, it's because of the air
conditioning.
So first of all, you know it's such a great honor
for this time to form this alliance with MIT,
and to be part of this MIT intelligent quest.
I think together we will definitely
go beyond just deep learning.
We will go to the uncharted territory of deep thinking.
Before I dive into that, let me start with something less deep.
Well I always start my talk with this picture for two reasons.
One is he's my son Samuel.
The second is he's just handsome.
And today, I do have a real reason.
In a few years, he's going to apply to college.
And, you know, perhaps MIT.
And we have so many professors today here, I just
want you to remember this face.
Yes I'm just joking.
Actually his math score is not very good.
It's not terrible, but it's kind of scary.
So I don't think he will make it into MIT,
we will settle for Harvard.
So enough kidding.
Let me talk about something something serious.
Money.
OK.
So this is a global top 10 box office last year
for all the movies.
And I read they are close to $1 billion each.
What is amazing about this picture is
that there is one movie among them is from China.
It's Wall of Warriors is the one right in the middle.
It actually beat out Wonder Woman and Paris of Caribbean.
It's an $850 million US dollar, which is amazing numbers.
I mean just 20 years ago, if we have 50 million,
that's a big movie.
And now it's $850 million, and it's a Chinese movie.
And all the other movies on this chart is global box office.
And this one, even though it's also global box office, but 99%
is from China.
Why?
Why can this movie make so much money?
Is it good really a great movie?
I actually watched it on the airplane.
And I think it's OK.
I have to say it's reasonably reasonable.
Why did it make so much money?
Because 20 years ago, nobody in China
will pay for a ticket, not nobody,
few will pay for a ticket to go to a theater to watch a movie.
Because you can't find a pirate copy anywhere easily.
So are not paying, so why make a movie.
But nowadays, people are going to the theaters.
They are paying.
So when you have the money, then creativity will follow.
I think that's the reason why we have such a great breakthrough.
And it's not just a one time thing.
It's actually this year, you know,
we are breaking the record again with several movies come out,
just making just a huge amount of money.
So when you have this protection of IP,
then you have the creativity and all the new things follow.
So let's come back to today's topic.
Today's topic is AI.
Creativity and AI.
So when I say the word AI, you know, what are you thinking?
Which company comes to your mind instantly?
I think we must be all thinking about save the company.
You know, Eric is smiling.
You know, great minds think alike.
Yes it's CSI.
Thank you.
Well instead of laughter, I'm looking for applause here.
[APPLAUSE]
Again, thank you for the sympathy.
So yes, it's Google.
Why Google?
Because Google did spend the money in tools.
15 is spotted for R&D, is 12 Billion.
You know, they threw away 12 billion dollar just
to do something that may work or may not work.
And then in 2014, they bought a company called DeepMind.
$606 million, 12 people, no product.
Just play.
Using deep learning to play but OK.
They could have just hired people one by one.
That way is much cheaper.
But they did spend $606 million.
That's the value for the people, for the talents.
And if they have not done that, we will not be of our goal.
And Apple Go really is amazing breakthrough,
and it advocated everyone on AI.
So what do you do?
How do you follow up with Apple Go.
Well, Google followed with Apple Go 2, the Apple Go 0.
What about other companies?
I think in China, quite a number of companies
are trying to develop their own go playing,
deploying our wisdoms.
But if you do that, you are just following the footstep
of what Google is doing.
In the ancient time before we have glass,
in China on the windows they had paper, paper windows.
So we have a saying, you poke a hole on the paper window,
then you will see what is inside that room.
So what Apple Go did is poke a hole.
Then we start to see a lot of amazing things.
So if you follow that with another game,
another learning network, all you
are doing is just poking another hole on the window.
You are not seeing anything new.
You're just seeing it from a slightly different angle.
So nothing really being created.
But what is really important is what you follow
and also what you have done before Apple Go.
So in 2014, we did something that
is not the same, but similar.
We developed a computer vision, [INAUDIBLE]
that can recognize a human face better
than a human being's eye.
So we break that record.
We beat Facebook to the punch and did it first.
And in the year follows, when we beat the human eye,
the accuracy we did is 98.5.
The human eye is 97.5.
And then next year we improved it to 99.5
with 300 face training, and a year later, we
improved it to one over a million accuracy,
using 16 million data for training.
Then a year later, we used two billion face data for training.
We reached the one over a 100 million
accuracy, which is equivalent to eight-digit password.
So we worked with Qualcomm from this alliance on AI.
And all this great work is done by MIT alumni.
Professor Xiaogang Wang is a student
of Professor Eric Grimson.
Also my student, a master's student, he came by MIT
and went back to CUHK as a professor.
A professor [INAUDIBLE],, the same, did a master in CUHK,
then come to MIT.
Worked with Eric.
Finished his PhD.
Now he's a professor in CUHK.
Now let me just show a few work we have done in our lab.
The first is to use pictures, to look at the pictures.
Use face expression and also the gestures of the people,
to people in the picture, to test their relationship.
We can do this based on expression
or based on gestures.
What is it useful for?
Because we can just take your picture from on your website
and see which picture you're taking
and what is the relationship.
You have taken a picture with rich people,
then the bank will lend you money easier.
If you take a picture with a criminal, then bad luck.
So we actually can do this in real time for videos.
But those two do not know how to throw a party.
[INTERPOSING VOICES]
So the red one is competitive and the green one
is a friendly.
You can see when they're arguing the red one will come up.
When they are not then [INAUDIBLE] in real time.
And I ask my students to try this on my kids' photos,
because my kids has a lot of girlfriends.
I just want to find out which one is real.
[LAUGHTER]
You know, the result come back.
They're all real.
But I just tell him that no matter how many friends you
have in the daytime, in the evening,
you can only take one home.
[LAUGHTER]
So I think it is a sensitive subject for dating.
Let me switch to another application, which
is the less sensitive--
politics.
[LAUGHTER]
I'm not from Russia.
So I want to help, not interfere.
I think in America, I've observed that sometimes people
will agree with an idea, but they don't like the messenger.
They like the message, not the messenger.
So with available technology that can help--
so if you don't like the messenger,
we can just do the face swap.
Change you to someone you like, then perhaps you will help.
And of course, we do it for both sides.
So if you don't like this messenger,
then we can switch you to the other messenger.
So this is all done in real time.
We can also even use your own face.
And the next application--
[MUSIC PLAYING]
We're watching the Olympics.
I think that for a long time, you just cannot see the real
action.
But what we can do using this technology
is to really pick up the highlights so
that you don't have to sit there for three hours.
Then you just-- nine minutes, then you watch other things.
And this one is--
we can
[MUSIC PLAYING]
search for other kinds of videos that you like.
For example, this is for disaster videos, and
[INTENSE MUSIC]
--this is for martial arts and for different types of movies.
And this one is trying to use natural language processing
to describe the scene and then come up
with a scene of the movie.
I apologize for this particular movie I am using.
I don't have time to change.
So this is Francis.
He's wearing a [INAUDIBLE] suit and a tie,
and he's seating himself beside Claire.
So let's pick up the scenes from the movie.
The next one is using natural language to describe the scene.
So basically it's like people are describing sports events
using machine.
A player in the blue shoots.
A player in white knocks the ball with a [INAUDIBLE]..
And this one is a movie.
It's called, Once Upon a Time in America.
So in this movie, we can detect everything
in the scene, including the tables, the players,
[MUSIC PLAYING]
--the actors, actress, almost everything in this.
So you this technology we can--
[FIGHTING SOUNDS]
--analyze the movie.
[INAUDIBLE] with description of what is happening and also
is it drama?
Is it action?
And everything.
[DARK MUSIC]
And this is, when we are watching the movie,
the machines tells what kind of action this is.
Is this action?
This part is action?
Oh, this part is a romantic.
[RANDOM SOUNDS]
And this one is picking out exciting moments in the movie.
[FIGHTING SOUNDS]
Because of the time, I just skip this.
[MUSIC PLAYING]
So basically what we are doing, we
are teaching the machines to watch movies for humans.
So the mind is teaching machine to play golf
and we have people teaching a machine
to play poker, play game, and we are now
teaching them to watch a movie.
So the machine is doing all the fun stuff.
What we do-- we just do the hard work of research.
I think something is wrong here.
[LAUGHTER]
So let me come back just to today's topic.
I think instead of teaching--
the machine we have now, it's really capable of learning.
It's not thinking.
So I was with Josh, he talked today,
he said, in our lifetime, in his lifetime,
perhaps we will never have a machine who can think.
That maybe 20 years or maybe even in my lifetime,
in 50 years I can probably never see this.
But you start off really to come up
with a machine who can think, who is intelligent.
Perhaps we can think by ourself.
We can think to to build an environment that can really
help scientists to solve the problem in AI.
And how to help scientists-- what scientists need the most.
As a scientist myself, I can tell you the only thing
we need is the money.
So just give us the money, and we will solve the problem.
So thank you, MIT, for giving me the opportunity
to learn from you when I was a student.
And thank you for giving me the opportunity
to think with you together in this IQ campaign.
Thank you very much.
[APPLAUSE]